Registration of dynamic contrast‐enhanced MRI of the common carotid artery using a fixed‐frame template‐based squared‐difference method

Abstract

Purpose This study examines template-based squared-difference registration for motion correction in dynamic contrast-enhanced (DCE) MRI studies of the carotid artery wall and compares the results of fixed-frame template-based registration with a previously proposed consecutive-frame registration method. Materials and Methods Ten T1-weighted black-blood, turbo spin-echo DCE-MRI studies of the carotid artery wall were used to test template-based squared-difference registration. An intermediate image from each series was selected as the fixed-frame template for registration. Squared-difference minimization was used to align each image and template. Time-intensity curves generated from data aligned with template-based squared-difference registration were compared with gold standard curves created by drawing regions of interest on each image in the series. The results were also compared with unregistered data and data after consecutive-frame squared-difference registration. Results An analysis of variance test of root mean-square error values between gold standard curve and curves from unregistered data and data registered with consecutive-frame and fixed-frame template-based methods was significant (P < 0.005) with template-based squared-difference registration producing curves that most closely matched the gold standard. Conclusion A fixed-frame template-based squared-difference registration method was proposed and validated for alignment of DCE-MRI of carotid arteries.

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment

“…DCE images were coregistered by using a rigid body registration algorithm which searches for the optimal rotation and translational parameters by minimizing the mean squared difference between the moving and the reference image as cost function [38].…”

Dynamic Contrast-Enhanced MRI to Study Atherosclerotic Plaque Microvasculature

“…Especially in the voxelwise analysis, movement of the subject during acquisition of the different DCE-MRI sequence time frames may pose a problem. A solution is to manually shift individual time frames to correctly align the images, or alternatively, to use post-processing methods for automated movement correction and noise reduction [ 20 , 22 ].…”

“…Overview of studies investigating the atherosclerotic plaque microvasculature using dynamic contrast-enhanced MRI: subjects (human or rabbits), analysis method (quantitative or semi-quantitative), main study purpose, and study outcome are shown Reference Subjects Main study purpose Main study outcome Chen et al [ 18 ] Patients with CVD (AIM-HIGH Trial [ 19 ] Scan-rescan reproducibility Moderate reproducibility for K trans (Patlak) with a 25 % coefficient of variation. To limit dropout, intensive operator training, optimized imaging, and quality control is required Kerwin et al [ 20 ] CEA patients Method development Development of a motion correcting and noise reducing algorithm for the analysis of DCE-MRI of carotid arteries Kerwin et al [ 21 ] Patients with a carotid lesion ≥ AHA type IV Method comparison Quantitative enhancement characteristics, such as K trans (Patlak), depend on the used contrast medium (gadobenate dimeglumine vs gadodiamide) Ramachandran et al [ 22 ] Humans with CVD risk Method development Development of a registration method for alignment of different time frames of DCE-MRI of carotid arteries Chen et al [ 23 ] Humans with advanced carotid disease Method development Extended graphical model exhibits a reduced bias in K trans estimation compared to the Patlak model Van Hoof et al [ 24 ] Symptomatic patients (30–99 % carotid stenosis) Method comparison Comparison between phase- and magnitude-based vascular input functions and resulting effect on pharmacokinetic parameters. No signal saturation due to blood flow for phase-based determined vascular input function Calcagno et al [ 25 ] Humans with CVD risk Method development Demonstration of feasibility of simultaneous VIF and vessel wall imaging (extended Tofts) Wan et al [ 26 ] NZW Rabbit a,b Method development Spatio-temporal texture based features (like AUC) are able to distinguish between vulnerable and stable plaques.…”

Effect of image registration on the estimation of pharmacokinetic parameters from DCE-MRI of patients with esophageal cancer

“…Several registration methods are available for registration of MR images, such as nonrigid-body registration for the kidneys 15 , deformable and rigid-body registration for prostate cancer 16 , rigid-body registration for gliomas 17 , and fixed-frame template-based squareddifference registration for abdominal DCE-MRI data on the liver 18 . The two most commonly used registration methods with medical images are deformable registration and rigid-body registration.…”